Abstract 5159: Towards population-scale screening of human cancer using genome-wide fragmentation profiles of cell-free DNA
Authors: Akshaya V. Annapragada, Dimitrios Mathios, Stephen Cristiano, Jamie E. Medina, Vilmos Adleff, Noushin Niknafs, Jacob Carey, Nic Dracopoli, Peter Bach, Jillian Phallen, Victor E. Velculescu, Robert B. Scharpf
Journal: Cancer Research (2022)

Abstract Introduction: Earlier detection is a critical clinical intervention to reduce cancer-related mortality. The DELFI liquid biopsy approach (DNA evaluation of fragments for early interception) utilizing low coverage (1-2x) whole genome sequencing (WGS) to analyze cell-free DNA (cfDNA) fragmentation provides a promising avenue for cancer detection. As sequencing costs remain a barrier to adoption of liquid biopsy approaches for early detection, we evaluated WGS for DELFI using 2-channel Illumina NovaSeq sequencing as a more affordable (~7-fold cost savings) alternative to 4-channel HiSeq instruments. Methods: We performed WGS on the prospectively collected LUCAS cohort of 365 individuals at risk for lung cancer using both HiSeq and NovaSeq platforms (Mathios et. al., Nature Communications 2021). Genome-wide fragmentation was summarized in non-overlapping 5 Mb bins by ratio of short (100-150 bp) to long (151-220 bp) fragments.To measure within-sequencer repeatability, we compared fragmentation profiles of non-cancer individuals to the median non-cancer fragmentation profile by Spearman correlation. Principal component analyses were performed to assess the extent to which the sequencer explains variation of fragmentation profiles across samples.For cancer prediction, we used a penalized logistic regression model with fragmentation profiles and other genome-wide characteristics as features. Machine learning performance was assessed by cross-validation and area under the receiver operator characteristic curve (AUC). To evaluate whether we could have developed the classifier from a combination of NovaSeq with HiSeq sequenced samples, we evaluated performance trained on 90:10%, 75:25%, 50:50%, 25:75%, and 10:90% HiSeq:NovaSeq mixtures, respectively. Results: cfDNA fragmentation profiles were highly concordant among non-cancer individuals for both platforms with median correlations of 0.96 (IQR: 0.95 - 0.97) and 0.95 (IQR: 0.94 - 0.96). Visualization of fragmentation principal components did not reveal separation by sequencing platform. The DELFI approach applied to samples sequenced by NovaSeq recapitulated previously published performance measures based on HiSeq (AUC 0.90, 95% CI 0.86 - 0.94). In simulations of mixed-platform datasets, we found the same qualitative performance (AUC range: 0.893-0.902). Conclusions: cfDNA fragmentation profiles were similar between HiSeq and NovaSeq platforms, and classification accuracies from machine learning models trained on these platforms were equivalent. Our results indicate HiSeq and NovaSeq sequenced samples can be combined in models with no discernible loss in classification accuracy provided a balance of non-cancers and cancers are sequenced on both platforms. The lower cost of NovaSeq sequencing may enable wider adoption of genome-wide fragmentation-based approaches for cancer detection. Citation Format: Akshaya V. Annapragada, Dimitrios Mathios, Stephen Cristiano, Jamie E. Medina, Vilmos Adleff, Noushin Niknafs, Jacob Carey, Nic Dracopoli, Peter Bach, Jillian Phallen, Victor E. Velculescu, Robert B. Scharpf. Towards population-scale screening of human cancer using genome-wide fragmentation profiles of cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5159.